Perception-aware time optimal path parameterization for quadrotors

TL;DR

This paper presents an efficient perception-aware time optimal path parametrization algorithm for quadrotors with limited camera field of view, improving navigation by jointly considering perception constraints during planning.

Contribution

It introduces a novel algorithm that integrates perception constraints into time optimal path planning for quadrotors, validated through simulations and real-world experiments.

Findings

The algorithm effectively incorporates camera FOV limitations into path planning.

Simulations show the planned trajectories are trackable by state-of-the-art controllers.

Experimental validation confirms the algorithm's practical applicability.

Abstract

The increasing popularity of quadrotors has given rise to a class of predominantly vision-driven vehicles. This paper addresses the problem of perception-aware time optimal path parametrization for quadrotors. Although many different choices of perceptual modalities are available, the low weight and power budgets of quadrotor systems makes a camera ideal for on-board navigation and estimation algorithms. However, this does come with a set of challenges. The limited field of view of the camera can restrict the visibility of salient regions in the environment, which dictates the necessity to consider perception and planning jointly. The main contribution of this paper is an efficient time optimal path parametrization algorithm for quadrotors with limited field of view constraints. We show in a simulation study that a state-of-the-art controller can track planned trajectories, and we validate the proposed algorithm on a quadrotor platform in experiments.